1. Field of the Invention
The invention described herein relates to a digital communication channel or link parameter monitor which can be used as a Bit Error Rate Monitor.
A communication channel or link parameter monitor can be used to assess a digital communication link or channel. By such assessment, information can be determined as to the nature of the Bit Error Rate associated with transmission of digital signals across the communication channel or link, as well as other channel or link parameters such as signal to noise ratio, fading and the type of noise affecting the link.
The present invention can be applied to any channel and terminal equipment through which digital signals are transmitted which constitute a digital communication channel or link and may be applied to any digital service including voice, data and video.
The present invention is not limited to the radio, telephony and like communication systems but can find application in any system where communication occurs and where continuous signals are used to communicate information, and such signals are interpreted into one of a plurality of symbols. Such systems include for example, optical disk reading, digital magnetic tape reading, compact disk audio disks and magnetic hard disks.
It will be appreciated that whilst the present invention is described herein in relation to communication between binary digital systems, it can be applied to communication between digital systems characterised by another number system base; for example ternary.
The present invention was conceived whilst conducting research into methods of determining a bit error rate associated with digital communication links. It has been realised that the present invention is applicable to a number of channel or link state parameters besides bit error rate. More will be said below in regard to the application of the present invention to other link state parameters.
2. Description of the Related Art
A link or channel state parameter is any parameter which characterises a communication link or channel. Examples of link state parameters are signal to noise ratio, eye pattern, interference type and bit error rate.
A communication channel is normally defined as the medium connecting a transmitter and a receiver. In the case of a radio link, the channel is often the atmosphere between the transmitting antenna and the receiving antenna; for an optical link, the channel would be the optical fibre.
A communication link is normally considered to include the following physical stages of a communication system: encoder, modulator, transmitter, channel, receiver, demodulator and decoder. The term link herein, unless contrary to the context, means the stages of a communication link listed above except for the decoder. This is because the received signal is tested prior to decoding. It will be appreciated that the majority of disturbances occur prior to this last stage.
In the situation of reading magnetic tape which has information digitally stored upon it, the channel mentioned above is the magnetic medium and a decision variable is the continuous valued signal which the detector interprets as digital information.
A demodulator within a receiver of a digital communication system uses a decision variable to interpret what symbol was sent. Whilst not limiting the meaning of decision variable, a decision variable is the parameter of the received signal which the demodulator interprets as a symbol. As examples, in the case of binary phase modulation, a decision variable is the phase component of the received signal; for an amplitude modulated signal the decision variable is the amplitude of the received signal after matched filtering; and for quadrature amplitude modulation there are two decision variables associated with phase and amplitude of the received signal respectively. In baseband systems, decision variable recovery is achieved by a line decoder which may be considered as an equivalent, in this regard, to a demodulator.
A decision variable is a continuous valued signal which the demodulator decodes into discrete valued signals. The decision variable is a continuous valued signal because of noise and other disturbances affecting the transmission of a symbol from a transmitter to a receiver. The value that the decision variable can have due to the effect of such disturbances is considered to follow a probability density function. That is, there is a probability that the decision variable will be a certain value at any particular time. The decision variable, in general, can be considered as a continuous random variable.
As a consequence of the continuous valued nature of a decision variable the associated probability density function will be continuous, and in general will change from time to time.
Probability density functions are often drawn as a histogram. In keeping with accepted probability theory nomenclature, it will be understood that the term "histogram" herein includes data which may be used to draw a histogram.
A bit error rate monitor is used to monitor and measure the bit error rate associated with a communication channel and can be applied to any communication channel through which digital signals are transmitted.
A bit error rate of a link or channel is the number of information bits communicated over the link that are interpreted, when received, as being bits of a type different from that which was transmitted. For example, in a binary system, if a bit is interpreted as a "1" when it was sent as a "0", then a bit error has occurred, and the numerical ratio of the number of bits in error to the total number of bits sent over some period of time is the bit error rate.
There are two basic types of bit error rate monitors. First, there are Active Error Rate Monitors and secondly, there are Passive Error Rate Monitors.
Active error rate monitoring techniques require that a known data stream be transmitted through the communication channel. The received data stream is compared with the known data that should have been received. The number of data bits incorrectly received over a period time is the bit error rate.
This method requires the data communicated to be stopped and the test data stream to be sent, thus imposing overheads on the communication channel. A further problem with this method is that for reasonably accurate bit error rates, to be calculable a large number of bits forming the test data stream must be sent. The necessary measurement interval T can be expressed as ##EQU1##
This measurement time is often prohibitively long even for quite high error rates on low rate channels, e.g. 33 minutes to determine a 10.sup.-4 bit error rate for a 50 bits per second link, or 17 minutes to determine a 10.sup.-8 bit error rate on a 10.sup.6 bits per second link. Further, as the standard deviation of the measured error counts may be approximated by the square root of the number of counts, even if 100 counts are registered then a 95% confidence interval is bounded by 75 and 125 counts (or approximately 25% of an order of magnitude).
Passive error rate estimation may be sub-divided into three basic categories. The first is used where forward error correction or error detection is employed. In this first case, the decoder may be interrogated for the bit error rate calculated from a number of detected bit errors over some measurement interval.
A major problem with this first method is that not all error patterns are detectable for a given code and a large amount of time must be spent counting the number of erroneous digits to give a statistically meaningful bit error rate. Also, the measurement time T is the same as for the active error rate measurement method. Consequently, a large amount of time must be spent counting the number of erroneous digits to give a generally useful bit error rate estimate.
The second category is one in which the signal parameters of the communication channel or link are measured. These could be a signal to noise ratio, fade rate, eye pattern opening, timing jitter, etc. As will be appreciated, all these methods are measuring parameters which are not directly related to the bit error rate. Consequently, a large error may be associated with this second of method because the measurements are not of the bit error rate but of other parameters which may not be directly related to the bit error rate, or may be highly sensitive to the channel or link statistics, or may be both highly sensitive and not directly related to the bit error rate.
In this second method, there is also a requirement that the relationship between channel parameters and bit error rate be known, and that bit error rate be solely dependent on measured channel parameters. The consequence of not meeting these stringent requirements is that estimations of the bit error rate may be inaccurate.
The final category of passive error rate monitoring schemes may be referred to as Pseudo-Error Rate Measurement. There are two forms, firstly, the Additive Noise Method and secondly, Lower Threshold Method.
The additive noise method is one in which White Gaussian Noise is added to a receiver decision variable or variables to increase the likelihood of exceeding a decision threshold. The receiver decision variable or variables, as mentioned above, is that signal or collection of signals just prior to the data decision threshold in any digital demodulator or baseband transmission scheme, and is the signal used to decide as to the most likely transmitted data bits.
Performing an EXCLUSIVE OR function between the pseudo-data stream and the normal demodulated data stream will thus produce approximately one pulse output per pseudo-error. The pseudo-data stream is the output of the receiver/demodulator which has had white Gaussian noise added to it. A given pseudo-error rate corresponds to an actual error rate, so from the pseudo-error rate an estimate of the actual bit error rate can be made using a look-up table.
The problem with this additive noise method is that if more than one class of link probability distribution is necessary, there will need to be a corresponding number of look-up tables. Further, it may be difficult to know what class of link probability distribution was present during a measurement interval.
The lower threshold method alternatively requires thresholds be set which are below the actual decision threshold. A counter is incremented each time the receiver decision variable, during a symbol period, exceeds a given decision threshold. The numerical ratio of the number of counts registered over some period of time to the total number of bits sent, is referred to as a pseudo-error rate. These pseudo-error rates, which will be greater than the actual error rate, are used to estimate the actual error rate by way of extrapolation.
The bulk of research into the Lower Threshold method was inspired by D. J. Gooding, "Performance Monitor Techniques for Digital Receivers Based on Extrapolation of Error Rate", IEEE Transactions on Communication Technology Vol COM-16 No. 3, June 1968. This research may be taken as the nearest known prior art.
The method described by Gooding requires the use of values for two or more counters with different thresholds which are then extrapolated to estimate the actual error rate. The extrapolation process is usually a linear extrapolation in a two dimensional space where the dimensions are the logarithm of pseudo-error rates versus a pre-determined function of threshold values.
The line is extrapolated in the function of threshold values dimension to the position where the threshold, value is the decision threshold then the logarithm or error rate is taken from the other dimension. This method is designed to extrapolate correctly for probability distributions of an exponential form.
Unfortunately, many probability distributions do not have an exponential form. Hence, the extrapolation distance must be short to estimate the bit error rate accurately. The same can be said of any model for the bit error rate using extrapolation be it an exponential model function or otherwise.